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Learning from Ranters : The Effect of Information Resistance on the Epistemic Quality of Social Network Deliberation

Morreau, Michael and Olsson, Erik J LU (2022)
Abstract
People who spread misinformation in public debates expose others to the risk of forming false beliefs. Excluding them from participation can limit this exposure, but fact-checking takes up resources of time and money, and censorship violates social and political norms. Here, computer simulations of Bayesian learning in social networks suggest that, in some contexts anyway, the epistemic benefits of excluding sources of misinformation might be small or nonexistent, and not worth associated costs. It is shown more specifically that, under certain conditions, open-minded agents in a network can learn just as well in the presence of false ranters: information resistant agents that repeatedly broadcast falsity within the network. Relevant... (More)
People who spread misinformation in public debates expose others to the risk of forming false beliefs. Excluding them from participation can limit this exposure, but fact-checking takes up resources of time and money, and censorship violates social and political norms. Here, computer simulations of Bayesian learning in social networks suggest that, in some contexts anyway, the epistemic benefits of excluding sources of misinformation might be small or nonexistent, and not worth associated costs. It is shown more specifically that, under certain conditions, open-minded agents in a network can learn just as well in the presence of false ranters: information resistant agents that repeatedly broadcast falsity within the network. Relevant conditions are that the open-minded agents can keep track of their social sources and maintain appropriate levels of trust in them, and that some sufficiently reliable sources introduce truth into the network. (Less)
Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Social Virtue Epistemology
editor
Alfano, Mark ; Klein, Colin and de Ridder, Jeroen
pages
19 pages
publisher
Routledge
external identifiers
  • scopus:85138356806
ISBN
9780367808952
9780367407643
DOI
10.4324/9780367808952-74
project
Filterbubblor och ideologisk segregering online: behövs reglering av sökmaskiner?
language
English
LU publication?
yes
id
d4a19da5-75b4-43f8-b124-bfc444c3066c
date added to LUP
2021-03-08 16:17:48
date last changed
2024-07-11 15:47:18
@inbook{d4a19da5-75b4-43f8-b124-bfc444c3066c,
  abstract     = {{People who spread misinformation in public debates expose others to the risk of forming false beliefs. Excluding them from participation can limit this exposure, but fact-checking takes up resources of time and money, and censorship violates social and political norms. Here, computer simulations of Bayesian learning in social networks suggest that, in some contexts anyway, the epistemic benefits of excluding sources of misinformation might be small or nonexistent, and not worth associated costs. It is shown more specifically that, under certain conditions, open-minded agents in a network can learn just as well in the presence of false ranters: information resistant agents that repeatedly broadcast falsity within the network. Relevant conditions are that the open-minded agents can keep track of their social sources and maintain appropriate levels of trust in them, and that some sufficiently reliable sources introduce truth into the network.}},
  author       = {{Morreau, Michael and Olsson, Erik J}},
  booktitle    = {{Social Virtue Epistemology}},
  editor       = {{Alfano, Mark and Klein, Colin and de Ridder, Jeroen}},
  isbn         = {{9780367808952}},
  language     = {{eng}},
  month        = {{07}},
  publisher    = {{Routledge}},
  title        = {{Learning from Ranters : The Effect of Information Resistance on the Epistemic Quality of Social Network Deliberation}},
  url          = {{http://dx.doi.org/10.4324/9780367808952-74}},
  doi          = {{10.4324/9780367808952-74}},
  year         = {{2022}},
}